Carga y limpieza preliminar de los datos
Los datos que se van a analizar en este documento, proceden de la
compilación hecha por usuarios de Kaggle.
La fecha del análisis empieza el 6 de Abril de 2020, utilizando la
versión número 166 recopilada en la web anterior.
import pandas as pd
datos = pd.read_csv("covid_19_clean_complete.csv")
datos.head(10)
## Province/State Country/Region ... Deaths Recovered
## 0 NaN Afghanistan ... 0 0
## 1 NaN Albania ... 0 0
## 2 NaN Algeria ... 0 0
## 3 NaN Andorra ... 0 0
## 4 NaN Angola ... 0 0
## 5 NaN Antigua and Barbuda ... 0 0
## 6 NaN Argentina ... 0 0
## 7 NaN Armenia ... 0 0
## 8 Australian Capital Territory Australia ... 0 0
## 9 New South Wales Australia ... 0 0
##
## [10 rows x 8 columns]
pd <- import("pandas")
datos <- pd$read_csv("covid_19_clean_complete.csv")
kable(head(datos, 10))
| NaN |
Afghanistan |
33.0000 |
65.0000 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Albania |
41.1533 |
20.1683 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Algeria |
28.0339 |
1.6596 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Andorra |
42.5063 |
1.5218 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Angola |
-11.2027 |
17.8739 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Antigua and Barbuda |
17.0608 |
-61.7964 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Argentina |
-38.4161 |
-63.6167 |
1/22/20 |
0 |
0 |
0 |
| NaN |
Armenia |
40.0691 |
45.0382 |
1/22/20 |
0 |
0 |
0 |
| Australian Capital Territory |
Australia |
-35.4735 |
149.0124 |
1/22/20 |
0 |
0 |
0 |
| New South Wales |
Australia |
-33.8688 |
151.2093 |
1/22/20 |
0 |
0 |
0 |
datos <- read.csv("covid_19_clean_complete.csv", stringsAsFactors = FALSE)
datos %>% head(10) %>% kable()
|
Afghanistan |
33.0000 |
65.0000 |
1/22/20 |
0 |
0 |
0 |
|
Albania |
41.1533 |
20.1683 |
1/22/20 |
0 |
0 |
0 |
|
Algeria |
28.0339 |
1.6596 |
1/22/20 |
0 |
0 |
0 |
|
Andorra |
42.5063 |
1.5218 |
1/22/20 |
0 |
0 |
0 |
|
Angola |
-11.2027 |
17.8739 |
1/22/20 |
0 |
0 |
0 |
|
Antigua and Barbuda |
17.0608 |
-61.7964 |
1/22/20 |
0 |
0 |
0 |
|
Argentina |
-38.4161 |
-63.6167 |
1/22/20 |
0 |
0 |
0 |
|
Armenia |
40.0691 |
45.0382 |
1/22/20 |
0 |
0 |
0 |
| Australian Capital Territory |
Australia |
-35.4735 |
149.0124 |
1/22/20 |
0 |
0 |
0 |
| New South Wales |
Australia |
-33.8688 |
151.2093 |
1/22/20 |
0 |
0 |
0 |
Estructura de los datos
str(datos)
## 'data.frame': 21484 obs. of 8 variables:
## $ Province.State: chr "" "" "" "" ...
## $ Country.Region: chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Lat : num 33 41.2 28 42.5 -11.2 ...
## $ Long : num 65 20.17 1.66 1.52 17.87 ...
## $ Date : chr "1/22/20" "1/22/20" "1/22/20" "1/22/20" ...
## $ Confirmed : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Deaths : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Recovered : int 0 0 0 0 0 0 0 0 0 0 ...
colnames(datos) = c("Provincia_Estado",
"Pais_Region",
"Latitud", # N+ o S-
"Longitud", # E+ o W-
"Fecha",
"Casos_Confirmados",
"Casos_Muertos",
"Casos_Recuperados"
)
datos %>% head() %>% kable() # %>% kable_styling()
|
Afghanistan |
33.0000 |
65.0000 |
1/22/20 |
0 |
0 |
0 |
|
Albania |
41.1533 |
20.1683 |
1/22/20 |
0 |
0 |
0 |
|
Algeria |
28.0339 |
1.6596 |
1/22/20 |
0 |
0 |
0 |
|
Andorra |
42.5063 |
1.5218 |
1/22/20 |
0 |
0 |
0 |
|
Angola |
-11.2027 |
17.8739 |
1/22/20 |
0 |
0 |
0 |
|
Antigua and Barbuda |
17.0608 |
-61.7964 |
1/22/20 |
0 |
0 |
0 |
- Cualitativas se convierten con
factor o bien
as.factor.
- Ordinales se convierten con
ordered.
- Cuantitativos se convierten con
as.numeric.
datos$Provincia_Estado %<>% factor()
datos$Pais_Region %<>% factor()
#datos$Fecha %<>% as.Date(format="%m/%d/%y")
datos$Fecha %<>% mdy()
str(datos)
## 'data.frame': 21484 obs. of 8 variables:
## $ Provincia_Estado : Factor w/ 81 levels "","Alberta","Anguilla",..: 1 1 1 1 1 1 1 1 6 49 ...
## $ Pais_Region : Factor w/ 185 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 9 ...
## $ Latitud : num 33 41.2 28 42.5 -11.2 ...
## $ Longitud : num 65 20.17 1.66 1.52 17.87 ...
## $ Fecha : Date, format: "2020-01-22" "2020-01-22" ...
## $ Casos_Confirmados: int 0 0 0 0 0 0 0 0 0 0 ...
## $ Casos_Muertos : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Casos_Recuperados: int 0 0 0 0 0 0 0 0 0 0 ...
\[Confirmados = Muertos + Recuperados +
Enfermos\]
datos %<>%
mutate(Casos_Enfermos = Casos_Confirmados - Casos_Muertos - Casos_Recuperados)
datos %>%
filter(Casos_Confirmados > 10000) %>%
head(10) %>%
kable()
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-02 |
11177 |
350 |
295 |
10532 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-03 |
13522 |
414 |
386 |
12722 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-04 |
16678 |
479 |
522 |
15677 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-05 |
19665 |
549 |
633 |
18483 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-06 |
22112 |
618 |
817 |
20677 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-07 |
24953 |
699 |
1115 |
23139 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-08 |
27100 |
780 |
1439 |
24881 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-09 |
29631 |
871 |
1795 |
26965 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-10 |
31728 |
974 |
2222 |
28532 |
| Hubei |
China |
30.9756 |
112.2707 |
2020-02-11 |
33366 |
1068 |
2639 |
29659 |
datos %>%
filter(Casos_Enfermos < 0) %>%
arrange(Provincia_Estado, Fecha) %>%
kable()
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-22 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-23 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-24 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-25 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-26 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-27 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-28 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-29 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-30 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-03-31 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-01 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-02 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-03 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-04 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-05 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-06 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-07 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-08 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-09 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-10 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-11 |
0 |
1 |
0 |
-1 |
| Diamond Princess |
Canada |
0.0000 |
0.0000 |
2020-04-12 |
-1 |
1 |
0 |
-2 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-24 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-25 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-26 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-27 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-28 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-29 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-30 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-31 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-01 |
168 |
6 |
168 |
-6 |
datos %>%
filter(Provincia_Estado == "Hainan") %>%
kable()
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-22 |
4 |
0 |
0 |
4 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-23 |
5 |
0 |
0 |
5 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-24 |
8 |
0 |
0 |
8 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-25 |
19 |
0 |
0 |
19 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-26 |
22 |
0 |
0 |
22 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-27 |
33 |
1 |
0 |
32 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-28 |
40 |
1 |
0 |
39 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-29 |
43 |
1 |
0 |
42 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-30 |
46 |
1 |
1 |
44 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-01-31 |
52 |
1 |
1 |
50 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-01 |
62 |
1 |
1 |
60 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-02 |
64 |
1 |
4 |
59 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-03 |
72 |
1 |
4 |
67 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-04 |
80 |
1 |
5 |
74 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-05 |
99 |
1 |
5 |
93 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-06 |
106 |
1 |
8 |
97 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-07 |
117 |
2 |
10 |
105 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-08 |
124 |
2 |
14 |
108 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-09 |
131 |
3 |
19 |
109 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-10 |
138 |
3 |
19 |
116 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-11 |
144 |
3 |
20 |
121 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-12 |
157 |
4 |
27 |
126 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-13 |
157 |
4 |
30 |
123 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-14 |
159 |
4 |
43 |
112 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-15 |
162 |
4 |
39 |
119 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-16 |
162 |
4 |
52 |
106 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-17 |
163 |
4 |
59 |
100 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-18 |
163 |
4 |
79 |
80 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-19 |
168 |
4 |
84 |
80 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-20 |
168 |
4 |
86 |
78 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-21 |
168 |
4 |
95 |
69 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-22 |
168 |
4 |
104 |
60 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-23 |
168 |
5 |
106 |
57 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-24 |
168 |
5 |
116 |
47 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-25 |
168 |
5 |
124 |
39 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-26 |
168 |
5 |
129 |
34 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-27 |
168 |
5 |
131 |
32 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-28 |
168 |
5 |
133 |
30 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-02-29 |
168 |
5 |
148 |
15 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-01 |
168 |
5 |
149 |
14 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-02 |
168 |
5 |
151 |
12 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-03 |
168 |
5 |
155 |
8 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-04 |
168 |
5 |
158 |
5 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-05 |
168 |
6 |
158 |
4 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-06 |
168 |
6 |
158 |
4 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-07 |
168 |
6 |
158 |
4 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-08 |
168 |
6 |
159 |
3 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-09 |
168 |
6 |
159 |
3 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-10 |
168 |
6 |
159 |
3 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-11 |
168 |
6 |
159 |
3 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-12 |
168 |
6 |
160 |
2 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-13 |
168 |
6 |
160 |
2 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-14 |
168 |
6 |
160 |
2 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-15 |
168 |
6 |
160 |
2 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-16 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-17 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-18 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-19 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-20 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-21 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-22 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-23 |
168 |
6 |
161 |
1 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-24 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-25 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-26 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-27 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-28 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-29 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-30 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-31 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-01 |
168 |
6 |
168 |
-6 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-02 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-03 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-04 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-05 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-06 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-07 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-08 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-09 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-10 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-11 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-12 |
168 |
6 |
162 |
0 |
datos %>%
filter(Provincia_Estado == "Hainan", Casos_Enfermos < 0) %>%
mutate(Casos_Recuperados = Casos_Recuperados + Casos_Enfermos,
Casos_Enfermos = 0) %>%
kable()
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-24 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-25 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-26 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-27 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-28 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-29 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-30 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-03-31 |
168 |
6 |
162 |
0 |
| Hainan |
China |
19.1959 |
109.7453 |
2020-04-01 |
168 |
6 |
162 |
0 |
Análisis geográfico
#datos_europa = datos[datos$Latitud > 38 & datos$Longitud > -25 & datos$Longitud < 30 , ]
datos_europa = datos %>%
filter(Latitud > 38, between(Longitud, -25, 30))
nrow(datos_europa)
## [1] 3690
table(datos_europa$Pais_Region) %>%
as.data.frame() %>%
filter(Freq > 0) %>%
kable()
| Albania |
82 |
| Andorra |
82 |
| Austria |
82 |
| Belarus |
82 |
| Belgium |
82 |
| Bosnia and Herzegovina |
82 |
| Bulgaria |
82 |
| Croatia |
82 |
| Czechia |
82 |
| Denmark |
164 |
| Estonia |
82 |
| Finland |
82 |
| France |
82 |
| Germany |
82 |
| Greece |
82 |
| Holy See |
82 |
| Hungary |
82 |
| Iceland |
82 |
| Ireland |
82 |
| Italy |
82 |
| Kosovo |
82 |
| Latvia |
82 |
| Liechtenstein |
82 |
| Lithuania |
82 |
| Luxembourg |
82 |
| Moldova |
82 |
| Monaco |
82 |
| Montenegro |
82 |
| Netherlands |
82 |
| North Macedonia |
82 |
| Norway |
82 |
| Poland |
82 |
| Portugal |
82 |
| Romania |
82 |
| San Marino |
82 |
| Serbia |
82 |
| Slovakia |
82 |
| Slovenia |
82 |
| Spain |
82 |
| Sweden |
82 |
| Switzerland |
82 |
| United Kingdom |
246 |
datos_europa %>%
filter(Fecha == ymd("2020-03-15")) %>%
kable()
|
Albania |
41.15330 |
20.16830 |
2020-03-15 |
42 |
1 |
0 |
41 |
|
Andorra |
42.50630 |
1.52180 |
2020-03-15 |
1 |
0 |
1 |
0 |
|
Austria |
47.51620 |
14.55010 |
2020-03-15 |
860 |
1 |
6 |
853 |
|
Belarus |
53.70980 |
27.95340 |
2020-03-15 |
27 |
0 |
3 |
24 |
|
Belgium |
50.83330 |
4.00000 |
2020-03-15 |
886 |
4 |
1 |
881 |
|
Bosnia and Herzegovina |
43.91590 |
17.67910 |
2020-03-15 |
24 |
0 |
0 |
24 |
|
Bulgaria |
42.73390 |
25.48580 |
2020-03-15 |
51 |
2 |
0 |
49 |
|
Croatia |
45.10000 |
15.20000 |
2020-03-15 |
49 |
0 |
1 |
48 |
|
Czechia |
49.81750 |
15.47300 |
2020-03-15 |
253 |
0 |
0 |
253 |
| Faroe Islands |
Denmark |
61.89260 |
-6.91180 |
2020-03-15 |
11 |
0 |
0 |
11 |
|
Denmark |
56.26390 |
9.50180 |
2020-03-15 |
864 |
2 |
1 |
861 |
|
Estonia |
58.59530 |
25.01360 |
2020-03-15 |
171 |
0 |
1 |
170 |
|
Finland |
64.00000 |
26.00000 |
2020-03-15 |
244 |
0 |
10 |
234 |
|
France |
46.22760 |
2.21370 |
2020-03-15 |
4499 |
91 |
12 |
4396 |
|
Germany |
51.00000 |
9.00000 |
2020-03-15 |
5795 |
11 |
46 |
5738 |
|
Greece |
39.07420 |
21.82430 |
2020-03-15 |
331 |
4 |
8 |
319 |
|
Holy See |
41.90290 |
12.45340 |
2020-03-15 |
1 |
0 |
0 |
1 |
|
Hungary |
47.16250 |
19.50330 |
2020-03-15 |
32 |
1 |
1 |
30 |
|
Iceland |
64.96310 |
-19.02080 |
2020-03-15 |
171 |
5 |
8 |
158 |
|
Ireland |
53.14240 |
-7.69210 |
2020-03-15 |
129 |
2 |
0 |
127 |
|
Italy |
43.00000 |
12.00000 |
2020-03-15 |
24747 |
1809 |
2335 |
20603 |
|
Latvia |
56.87960 |
24.60320 |
2020-03-15 |
30 |
0 |
1 |
29 |
|
Liechtenstein |
47.14000 |
9.55000 |
2020-03-15 |
4 |
0 |
0 |
4 |
|
Lithuania |
55.16940 |
23.88130 |
2020-03-15 |
12 |
0 |
1 |
11 |
|
Luxembourg |
49.81530 |
6.12960 |
2020-03-15 |
59 |
1 |
0 |
58 |
|
Moldova |
47.41160 |
28.36990 |
2020-03-15 |
23 |
0 |
0 |
23 |
|
Monaco |
43.73330 |
7.41670 |
2020-03-15 |
2 |
0 |
0 |
2 |
|
Montenegro |
42.50000 |
19.30000 |
2020-03-15 |
0 |
0 |
0 |
0 |
|
Netherlands |
52.13260 |
5.29130 |
2020-03-15 |
1135 |
20 |
2 |
1113 |
|
North Macedonia |
41.60860 |
21.74530 |
2020-03-15 |
14 |
0 |
1 |
13 |
|
Norway |
60.47200 |
8.46890 |
2020-03-15 |
1221 |
3 |
1 |
1217 |
|
Poland |
51.91940 |
19.14510 |
2020-03-15 |
119 |
3 |
0 |
116 |
|
Portugal |
39.39990 |
-8.22450 |
2020-03-15 |
245 |
0 |
2 |
243 |
|
Romania |
45.94320 |
24.96680 |
2020-03-15 |
131 |
0 |
9 |
122 |
|
San Marino |
43.94240 |
12.45780 |
2020-03-15 |
101 |
5 |
4 |
92 |
|
Serbia |
44.01650 |
21.00590 |
2020-03-15 |
48 |
0 |
0 |
48 |
|
Slovakia |
48.66900 |
19.69900 |
2020-03-15 |
54 |
0 |
0 |
54 |
|
Slovenia |
46.15120 |
14.99550 |
2020-03-15 |
219 |
1 |
0 |
218 |
|
Spain |
40.00000 |
-4.00000 |
2020-03-15 |
7798 |
289 |
517 |
6992 |
|
Sweden |
63.00000 |
16.00000 |
2020-03-15 |
1022 |
3 |
1 |
1018 |
|
Switzerland |
46.81820 |
8.22750 |
2020-03-15 |
2200 |
14 |
4 |
2182 |
| Channel Islands |
United Kingdom |
49.37230 |
-2.36440 |
2020-03-15 |
3 |
0 |
0 |
3 |
| Isle of Man |
United Kingdom |
54.23610 |
-4.54810 |
2020-03-15 |
0 |
0 |
0 |
0 |
|
United Kingdom |
55.37810 |
-3.43600 |
2020-03-15 |
1140 |
21 |
18 |
1101 |
|
Kosovo |
42.60264 |
20.90298 |
2020-03-15 |
0 |
0 |
0 |
0 |
\[d(x, y) = \sqrt{(x_{Lat}-y_{Lat})^2 +
(x_{Long}-y_{Long})^2}\]
distancia_grados = function(x, y){
sqrt((x[1]-y[1])^2 + (x[2]-y[2])^2)
}
distancia_grados_potsdam = function(x){
potsdam = c(52.366956, 13.906734)
distancia_grados(x, potsdam)
}
dist_potsdam = apply(cbind(datos_europa$Latitud, datos_europa$Longitud),
MARGIN = 1,
FUN = distancia_grados_potsdam)
datos_europa %<>%
mutate(dist_potsdam = dist_potsdam)
datos_europa %>%
filter(between(Fecha, dmy("2-3-2020"), dmy("7-3-2020")),
dist_potsdam < 4) %>%
kable()
|
Czechia |
49.8175 |
15.473 |
2020-03-02 |
3 |
0 |
0 |
3 |
2.992142 |
|
Czechia |
49.8175 |
15.473 |
2020-03-03 |
5 |
0 |
0 |
5 |
2.992142 |
|
Czechia |
49.8175 |
15.473 |
2020-03-04 |
8 |
0 |
0 |
8 |
2.992142 |
|
Czechia |
49.8175 |
15.473 |
2020-03-05 |
12 |
0 |
0 |
12 |
2.992142 |
|
Czechia |
49.8175 |
15.473 |
2020-03-06 |
18 |
0 |
0 |
18 |
2.992142 |
|
Czechia |
49.8175 |
15.473 |
2020-03-07 |
19 |
0 |
0 |
19 |
2.992142 |
world <- ne_countries(scale = "medium", returnclass = "sf")
datos$Pais_Region = factor(datos$Pais_Region, levels = c(levels(datos$Pais_Region), "United States"))
datos[datos$Pais_Region=="US",]$Pais_Region = "United States"
world %>%
inner_join(datos, by = c("name" = "Pais_Region")) %>%
filter(Fecha == dmy("30-03-2020")) %>%
ggplot() +
geom_sf(color = "black", aes(fill = Casos_Confirmados)) +
# coord_sf(crs="+proj=laea +lat_0=50 +lon_0=10 +units=m +ellps=GRS80") +
scale_fill_viridis_c(option="plasma", trans = "sqrt") +
xlab("Longitud") + ylab("Latitud") +
ggtitle("Mapa del mundo ", subtitle = "COVID 19") -> g
ggplotly(g)